A Shared Execution Strategy for Multiple Pattern Mining Requests over Streaming Data

In diverse applications ranging from stock trading to traffic mon-itoring, popular data streams are typically monitored by multiple analysts for patterns of interest. These analysts may submit similar pattern mining requests, such as cluster detection queries, yet customized with different parameter settings. In this work, we present an efficient shared execution strategy for processing a large num-ber of density-based cluster detection queries with arbitrary parameter settings. Given the high algorithmic complexity of the clustering process and the real-time responsiveness required by streaming applications, serving multiple such queries in a single system is extremely resource intensive. The naive method of detecting and maintaining clusters for different queries independently is often infeasible in practice, as its demands on system resources increase dramatically with the cardinality of the query workload. To over